This invention relates to a point-to-point ride sharing system that computes in real time optimal matching of ride sharing requests based on a shareability network.
Analytical and experimental studies based on real-world taxi date sets form different cities in the world (New York, San Francisco, Singapore, and Veinna) have shown a consistent, large potential for taxi ride sharing. In New York, more than 95% of taxi trips can be shared if a maximum delay parameter is set to five minutes, with an operational cost reduction on the order of 30%. Similar results have been obtained in other cities. An operational cost reduction on the order of 30% is very profitable for a taxi company, and allows the definition of a business model in which the benefits of ride sharing are distributed between customers (who pay a lower fare), drivers, and the taxi company itself.
Although several prior art systems have been introduced for real-time collection of trip requests (e.g., taxi e-haling and booking systems), only some of them consider the possibility of sharing rides among patrons. The common practice for ride sharing is that trip requests are elaborated in a sequential fashion: when a new request RA from patron A arrives, the request is checked for potential sharing with a pool of existing pending requests; depending on parameters such as pickup/dropoff points, patron profile, etc., some of these requests {R1, R2, . . . } are considered potentially sharable with RA. The pool {R1, R2 . . . } of potentially sharable rides is returned to patron A who then selects the preferred ride sharing option among them, i.e., the ride sharing decision is left to the patron. This method is highly ineffective since a composition of individual patron decisions is not guaranteed to find the system-wide optimal combination of shared trips—and actually in practice it is very likely to build a highly suboptimal solution.
An object of the present invention is a methodology in which trip requests are processed in batches instead of sequentially and trip sharing is decided by a centralized server instead of by a composition of individual customer decisions.